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Let every team self-serve Kafka inside guardrails, without infrastructure and administration growing at the same rate as adoption.

BlackRock's Jeff Miller closes CONTEXT 2026 with an inside look at the context layer behind Aladdin and how his team gets data ready for agents. Register free for the Nov 4 virtual summit.

Miro put data analytics agents into production. Hear how it went from Miro's Ronald Angel and DataHub co-founder John Joyce in a fireside chat at CONTEXT 2026, free and virtual on Nov 4.

NASA lost a spacecraft because one team sent thruster data in pound-force seconds, the other read it as newton-seconds. Don't make the same mistake with your reporting as NASA did. Their teams worked from different versions of the truth. Sounds familiar? Reports get manually recreated, adjusted and reconciled before anyone will trust them. Your people spend their time checking and debating the numbers, instead of using them. Book a data scan with All Your BI to decide on one source of truth.

Need to get your service back up and running? Missing data and slow search can delay your investigations and keep your customers waiting. With Bronto, you can store all of your logs, traces, and metrics in full-fidelity for 12 months, retained always hot and instantly searchable. Get reliable answers fast and keep building.

At $0.01 per GB ingested, you can keep all of your logs, traces, and metrics in Bronto for 12 months. Automatically parsed. Instantly searchable. Ready for any query, prompt, or agentic workflow. With all your observability data in one place, you can troubleshoot any error with confidence and surface performance trends over time.

Most Amazon issues ⚠️ show up when sales already slip. CATAPULT Retail Hub highlights early signals across Buy Box, stock, pricing, and traffic so you can easily see what’s changing and why. Want to see how it works? Book a short demo. 🔥 Or request free access and try it for 14 days.

Sales drops on Amazon rarely come from one place. That's why Retail Hub gives you early warning signals across retail KPIs, like Buy Box, stock, traffic or pricing. A single, fully customizable report makes sure you can spot the real cause fast and act with 100% confidence. Want to see how it works? Book a short demo. 🔥 Or request free access and try it for 14 days.

Get your next advantage. See Wave, which helps products learn from customer behaviour and self-improve, live in Berlin, 2 Dec.

Novartis Operations and TechOps drive supply reliability out of Ireland and Slovenia while keeping data maturity at 5+. Snowflake standardizes pipelines and puts supply data in front of AI agents, so plants get value in weeks. It shouldn't take a year to deliver AI.

Hiring a replacement takes months, and then you depend on one person all over again. We make the report run itself in 30 days instead.

The VertiGIS Annual Breakfast Seminar brings together GIS professionals, data leaders and asset managers to explore the latest trends in spatial data, geospatial technology and digital transformation.

Hear from industry experts and customers, discover how organisations are using data and automation to drive better outcomes, and get the latest updates from the FME ecosystem. Join us in Adelaide or Perth.

Your team tried AI and it didn't land. Swipe to see how we measure one real use case on your own work, in three weeks.

OT and IT still siloed? Every AI use case rebuilds the same integration.

No more hunting through heatmaps. Zoning Insights tells you exactly what's working.

If you read nothing else: Omni will soon support 100% custom data viz. That means if you can describe it, our AI can build it, and it runs on the definitions in your semantic layer. I’ve spent over 20 years working on data visualization software. Data visualization sits at the intersection of computer science, design, and data analysis. Because chart libraries are usually consumed by highly opinionated viewers, building a single chart that satisfies everyone is already difficult. In BI, coupling chart building to a generic tool is even harder - your library must handle data shapes it hasn't seen before. To use a video game analogy: this isn't hard mode, it's nightmare mode. You can’t make everyone happy all the time, so eventually you need a custom data viz escape hatch. That’s exactly what we’re building. Want a bespoke version of classic cartesian charts? Now you can have it. Want to create a one-off data viz to tell a story for your report? Go for it. Want to replicate your CEO’s favorite Economist chart aesthetic? Yep, you can do that too. Of course, we’re coupling this to Blobby, our AI agent. We're also making it available through our MCP connectors so you can use AI to build whatever you want, wherever you want. All of this stays backed by our governed data model, ensuring the right data is visualized for just the right viewers.

Omni is a launch partner in the SpaceX Super Intelligence Partner Network 🤝 We love building in Cursor, and so do many of our customers. Omni's Cursor plugin and MCP server bring governed data into those workflows, so you can query, define new metrics, and build data apps on the shared definitions your team trusts without leaving your preferred workspace 🛠️ Here's what that looks like end to end. Peter on our Solutions Engineering team built a demo customer portal for our sales team, starting from one paragraph in Cursor and ending with embedded dashboards and AI chat 👉 https://lnkd.in/g3u9RfAH Thanks to the SpaceX team for the partnership, and check out this blog from Peter for a deeper dive on Omni + Cursor.

Vous connaissez le rêve : des tableaux de bord en libre-service, moins de tickets, des décisions plus rapides. Vous connaissez aussi la réalité : des tableaux de bord que personne ne consulte, des « enseignements » fondés sur des métriques mal interprétées, et un backlog sans fin de retouches qui deviennent soudain urgentes dès qu'une réunion commence. Pendant ce temps, votre équipe continue de construire, et le métier continue de demander : « Et donc ? » « Dashboard Confessionals » rassemble des citations réelles et anonymes de data leaders et de leurs équipes. Ce qu'on n'avoue qu'une fois l'écran partagé refermé.

Your margin looks healthy at company level, but it is leaking somewhere you cannot see. That is not a data problem, as you already have the operational information. It sits in disconnected views. Operations on one side, finance on the other. So every process gets optimised on its own, and nobody sees what that does to total performance. Book a data scan with All Your BI, and find where your margin is leaking.

AI isn’t the challenge — your data is. Assess your foundations and unlock scalable, production‑ready AI outcomes.

91% of ISVs say they’re AI‑ready. The data tells a different story. Discover what’s holding teams back and how to fix it.

Most AI architecture diagrams have one small yellow box in the middle. That box is the model, and it gets almost all the attention. In every production AI system I have seen, the hard work sits in the boxes around it: evaluation, guardrails, tool permissions, tracing, cost control, monitoring. That is where pilots either become dependable systems or quietly stall. The numbers show it. McKinsey's State of AI 2026: 44% of organisations say AI is scaling across the enterprise, yet only 37% see any EBIT impact. One in five say running costs already hold them back. Agentics makes this layer even more important. Once AI plans, calls tools and acts on its own, you need to know what it did, what it was allowed to do, and what each completed task cost. That operating discipline is AgentOps. Today we publish paper 1 of our AgentOps Series, written by Partnership Professor @Darius Dilijonas for CTOs and heads of AI, data and platform. In fact it's written for anyone who is interested to learn more about the inner workings of Agent- and MLOps. Three questions from it that I'd put to any leadership team this quarter: → Does every production AI system have a named owner and a budget? → Can you show the cost per completed task for your agents? → Has anyone tested how to stop an agent? If any answer is "not yet", the article is a good place to start. https://lnkd.in/epyn2_Jz For those who want the depth, there is a free download of the full research report on our blog. #AgentOps #Agentics #MLOps #AIinProduction

The thinking isn't the slow part of an impact assessment. It's the chasing, the reminders, the handoffs, the wondering where a case got to and who has it now. Our Workflow Solution automates that coordination. Each case runs on a visual workflow you can see, edit, and control, with AI agents moving it from one stage to the next so the work doesn't stall between people. You review the agents' settings and set the controls, so the automation runs the process the way your team works, and you keep the final say. Regulatory updates land ready to assess, cases carry their own owners and due dates, and the whole thing stays in one place. Instead of managing the process, your team spends its time on the impact assessment itself. Assessments move faster, and the manual chase disappears. See where the Workflow Solution gives your team its time back.